Search Results (263)

Wireless sensor network data is an important source of big data. A sensor node cooperatively transmits or forwards data through intermediate nodes to a collection center, which is then aggregated for big data analysis and application. The relay selection algorithm selects the best transmissible node among the candidate nodes to fully exploit the limited resources of the sense nodes and extend the network lifecycle. A wireless sensor network relay selection algorithm based on a fuzzy inference system often uses sorting methods or random methods as the selection mechanism to choose when the fuzzy system outputs the same result. However, in the state of communication, networks often face the retransmission of lost packets, which consumes excess electricity. This study proposes a contraindicated safety selection mechanism algorithm to address equal output values in fuzzy systems. The proposed algorithm effectively reduces the retransmission probability to achieve benefits that isolate destructive or malicious nodes, thereby maintaining a higher network lifespan and safety. Full article

This study presents a new intelligent aquatic farming surveillance system that tackles real-time monitoring challenges in the industry. The main technical break-throughs of this system are evident in four key aspects: First, it achieves the smooth integration of remotely operated vehicles (ROVs), sensors, and real-time data transmission. Second, it uses a mobile communication architecture with buoy relay stations for distributed edge computing. This design supports future upgrades to Beyond 5G and satellite networks for deep-sea applications. Third, it features a multi-terminal control system that supports computers, smartphones, smartwatches, and centralized hubs, effectively enabling monitoring anytime, anywhere. Fourth, it incorporates a cost-effective modular design, utilizing commercial hardware and innovative system integration solutions, making it particularly suitable for farms with limited resources. The data indicates that the system’s 4G connection is both stable and reliable, demonstrating excellent performance in terms of data transmission success rates, control command response delays, and endurance. It has successfully processed 324,800 data transmission events, thoroughly validating its reliability in real-world production environments. This system integrates advanced technologies such as the Internet of Things, mobile communications, and multi-access control, which not only significantly enhance the precision oversight capabilities of marine farming but also feature a modular design that allows for future expansion into satellite communications. Notably, the system reduces operating costs while simultaneously improving aquaculture efficiency, offering a practical and intelligent solution for small farmers in resource-limited areas. Full article

As sensor technology integrates into modern life, diverse sensing devices have become essential for collecting critical data that enables human–machine interfaces such as autonomous vehicles and healthcare monitoring systems. However, the growing number of sensor devices places significant demands on network capacity, which is constrained by the limitations of radio frequency (RF) technology. RF-based communication faces challenges such as bandwidth congestion and interference in densely populated areas. To overcome these challenges, a combination of RF with free-space optical (FSO) communication is presented. FSO is a laser-based wireless solution that offers high data rates and secure communication, similar to fiber optics but without the need for physical cables. However, FSO is highly susceptible to atmospheric turbulence and conditions such as fog and smoke, which can degrade performance. By combining the strengths of both RF and FSO, a hybrid FSO/RF system can enhance network reliability, ensuring seamless communication in dynamic urban environments. This review examines hybrid FSO/RF systems, covering both theoretical models and real-world applications. Three categories of hybrid systems, namely hard switching, soft switching, and relay-based mechanisms, are proposed, with graphical models provided to improve understanding. In addition, multi-platform applications, including autonomous, unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and satellites, are presented. Finally, the paper identifies key challenges and outlines future research directions for hybrid communication networks. Full article

Cryptocurrency such as Bitcoin supports anonymous routing (Tor and I2P) due to the application requirements of anonymity and censorship resistance. In permissionless and open networking for cryptocurrency, an adversary can spoof to pretend to use Tor or I2P for anonymity and privacy protection, while, in reality, it is not using anonymous routing and is forwarding its networking directly to the destination peer to reduce networking overheads. Using profile detection based on deterministic features to detect anonymous routing and false claims is vulnerable to spoofing, especially in permissionless cryptocurrency bypassing registration control. We thus designed and built a method of network fingerprinting, using networking behaviors to detect and classify networking types. We built a network sensor to collect data on an active Bitcoin node connected to the Mainnet and applied supervised machine learning to identify whether a peer node was using IP (direct forwarding without the relays for anonymity protection), Tor, or I2P. Our results show that our scheme is effective in accurately detecting networking types and identifying spoofing attempts through supervised machine learning. We tested our scheme using multiple supervised learning models, specifically CatBoost, Random Forest, and HistGradientBoosting. CatBoost and Random Forest performed best and had comparable accuracy performance in effectively detecting false claims, i.e., they classified the networking types and detected fake claims of Tor usage with 93% accuracy and false claims of I2P with 94% accuracy in permissionless Bitcoin. However, CatBoost-based detection was significantly quicker than Random Forest and HistGradientBoosting in real-time testing and detection. Full article

Wireless sensor networks (WSNs) face a fundamental challenge: their sensors run on batteries, making energy efficiency crucial. While researchers have tried to extend network lifespans by improving routing and access control protocols across different layers, this remains a complex issue. One promising solution is opportunistic routing (OR), which uses multiple nodes to relay data. This approach reduces how long senders must wait for a specific next-hop node and helps prevent data loss from collisions. That said, choosing which nodes should act as forwarders can greatly affect how well the network performs. To tackle this problem, we developed a new approach called FCM-OR, a local density-based forwarder selection algorithm for opportunistic routing in WSNs. Our algorithm is particularly effective in networks where sensors are not evenly spread out and are densely packed. It uses fuzzy c-means (FCM) clustering to smartly pick the best forwarders based on how many nodes are nearby. By focusing on the sender’s immediate surroundings, FCM-OR helps solve the problems that arise when different parts of the network have varying densities of nodes. We also created a new way to measure routing effectiveness called “forwarding rank”. To test how well our protocol works, we ran extensive simulations comparing it to existing methods, including opportunistic routing with duty-cycled WSNs and load-balanced opportunistic routing. The results are clear: FCM-OR significantly improves both network performance and energy efficiency, especially in networks where nodes are not uniformly distributed. Full article

The underwater conditions of the coastal ecosystem require careful monitoring to anticipate potential environmental hazards. Moreover, the unique characteristics of the marine underwater environment have presented numerous challenges for the advancement of underwater sensor networks. Current studies have not extensively integrated Digital Twins with underwater sensor networks aimed at monitoring the marine ecosystem. Consequently, this study proposes a decision-making framework based on Underwater Digital Twins (UDTs) utilizing the Exponential Hyperbolic Crisp Adaptive Network-based Fuzzy Inference System (EHC-ANFIS). The process begins with the initialization and registration of an Underwater Autonomous Vehicle (UAV). Subsequently, data are collected from the sensor network and relayed to the UDT model. The optimal path is determined using Adaptive Pheromone Ant Colony Optimization (AP-ACO) to ensure efficient data transmission. Following this, data compression is achieved through the Sliding–Huffman Coding (SHC) algorithm. The Twisted Koblitz Curve Cryptography (TKCC) method is employed to enhance data security. Additionally, an Anomaly Detection System (ADS) is trained, which involves collecting and pre-processing sensor network data. A Radial Chart is then utilized for effective visualization. Anomalies are detected using the CosLU-Variational Shake-Long Short-Term Memory (CosLU-VS-LSTM) approach. For standard data, decision-making based on the UDT model is conducted using EHC-ANFIS, with a fuzzification duration of 21,045 milliseconds. Finally, alerts are dispatched to the Maritime Alert Command Centre (MACC). This approach enhances maritime communication and monitoring along coastal areas, with specific reference to the Coromandel Coast, thereby contributing to the protection of the coastal ecosystem. Full article

This work presents an innovative, energy-efficient IoT routing protocol that combines advanced data fusion grouping and routing strategies to effectively tackle the challenges of data management in smart cities. Our protocol employs hierarchical Data Fusion Head (DFH), relay DFHs, and marine predators algorithm, the latter of which is a reliable metaheuristic algorithm which incorporates a fitness function that optimizes parameters such as how closely the Sensor Nodes (SNs) of a data fusion group (DFG) are gathered together, the distance to the sink node, proximity to SNs within the data fusion group, the remaining energy (RE), the Average Scale of Building Occlusions (ASBO), and Primary DFH (PDFH) rotation frequency. A key innovation in our approach is the introduction of data fusion techniques to minimize redundant data transmissions and enhance data quality within DFG. By consolidating data from multiple SNs using fusion algorithms, our protocol reduces the volume of transmitted information, leading to significant energy savings. Our protocol supports both direct routing, where fused data flow straight to the sink node, and multi-hop routing, where a PDF relay is chosen based on an influential relay cost function that considers parameters such as RE, distance to the sink node, and ASBO. Given that the proposed protocol incorporates efficient failure recovery strategies, data redundancy management, and data fusion techniques, it enhances overall system resilience, thereby ensuring high protocol performance even in unforeseen circumstances. Thorough simulations and comparative analysis reveal the protocol’s superior performance across key performance metrics, namely, network lifespan, energy consumption, throughput, and average delay. When compared to the most recent and relevant protocols, including the Particle Swarm Optimization-based energy-efficient clustering protocol (PSO-EEC), linearly decreasing inertia weight PSO (LDIWPSO), Optimized Fuzzy Clustering Algorithm (OFCA), and Novel PSO-based Protocol (NPSOP), our approach achieves very promising results. Specifically, our protocol extends network lifespan by 299% over PSO-EEC, 264% over LDIWPSO, 306% over OFCA, and 249% over NPSOP. It also reduces energy consumption by 254% relative to PSO-EEC, 264% compared to LDIWPSO, 247% against OFCA, and 253% over NPSOP. The throughput improvements reach 67% over PSO-EEC, 59% over LDIWPSO, 53% over OFCA, and 50% over NPSOP. By fusing data and optimizing routing strategies, our protocol sets a new benchmark for energy-efficient IoT DFG, offering a robust solution for diverse IoT deployments. Full article

Underwater wireless sensor networks (UWSNs) widely used for maritime object detection or for monitoring of oceanic parameters that plays vital role prediction of tsunami to life-cycle of marine species by deploying sensor nodes at random locations. However, the dynamic and unpredictable underwater environment poses significant challenges in communication, including interference, collisions, and energy inefficiency. In changing underwater environment to make routing possible among nodes or/and base station (BS) an adaptive receiver-initiated deep adaptive with power control and collision avoidance MAC (DAWPC-MAC) protocol is proposed to address the challenges of interference, collisions, and energy inefficiency. The proposed framework is based on Deep Q-Learning (DQN) to optimize network performance by enhancing collision avoidance in a varying sensor locations, conserving energy in changing path loss with respect to time and depth and reducing number of relaying nodes to make communication reliable and ensuring synchronization. The dynamic and unpredictable underwater environment, shaped by variations in environmental parameters such as temperature (T) with respect to latitude, longitude, and depth, is carefully considered in the design of the proposed MAC protocol. Sensor nodes are enabled to adaptively schedule wake-up times and efficiently control transmission power to communicate with other sensor nodes and/or courier node plays vital role in routing for data collection and forwarding. DAWPC-MAC ensures energy-efficient and reliable time-sensitive data transmission, improving the packet delivery rati (PDR) by 14%, throughput by over 70%, and utility by more than 60% compared to existing methods like TDTSPC-MAC, DC-MAC, and ALOHA MAC. These enhancements significantly contribute to network longevity and operational efficiency in time-critical underwater applications. Full article

We introduce a robust distributed collaborative beamforming (RDCB) approach for addressing channel estimation challenges in dual-hop transmissions within wireless sensor and actuator networks (WSANs) of K nodes. WSANs enhance wireless communication by reducing data transmission, latency, and energy consumption while optimizing network load through integrated sensing and actuation. The source S transmits signals to the WSAN, where nodes relay them to the destination D using beamforming weights to minimize noise and preserve signal integrity. These weights depend on channel state information (CSI), where estimation errors degrade performance. We develop RDCB solutions for three first-hop propagation scenarios—monochromatic [line-of-sight (LoS)] or “M”, bichromatic (moderately scattered) or “B”, and polychromatic (highly scattered) or “P”—while assuming a monochromatic LoS or “M” link for the second hop between the nodes and the far-field destination. Termed MM-RDCB, BM-RDCB, and PM-RDCB, respectively (“X” and “Y” in XY-RDCB—for X $\in \{\mathrm{M},\mathrm{B},\mathrm{P}\}$ and Y $\in \left\{\mathrm{M}\right\}$—refer to the chromatic natures of the first- and second-hop channels, respectively, to which a specific RDCB solution is tailored), these solutions leverage asymptotic approximations for large K values and the nodes’ geometric symmetries. Our distributed solutions allow local weight computation, enhancing spectral and power efficiency. Simulation results show significant improvements in the signal-to-noise ratio (SNR) and robustness versus WSAN node placement errors, making the solutions well suited for emerging 5G and future 5G+/6G and Internet of Things (IoT) applications for different challenging environments. Full article

Standards describing the test procedures recommended to investigate the shielding effectiveness of enclosures have two major issues: they generally prescribe the assessment of the electromagnetic field of empty cavities, and they do not deal with very small enclosures. However, the dimensions of some very common shielded apparatus are smaller than those considered in the standards and the electromagnetic field distribution inside the shielded structure is strongly affected by the enclosure content. In this paper, both issues have been investigated for two components commonly used in medium voltage/low voltage (MV/LV) substations: a mini personal computer used to store, process, and transmit relevant data on the status of the electric network, with these aspects being essential in smart grids, and an electronic relay which is ubiquitous in MV/LV substations. Both components are partially contained in a metallic enclosure which provides a certain amount of electromagnetic shielding against external interferences. It is observed that an electrostatic discharge may cause a failure and/or a loss of data, requiring an improvement of shielding characteristics or a wise choice of the positions where the most sensitive devices are installed inside the enclosure. Since the dimensions of very small enclosures, fully occupied by their internal components, do not allow for the insertion of sensors inside the protected volume, numerical analysis is considered as the only way for the appraisal of the effects induced by a typical source of interference, such as an electrostatic discharge. Full article

Wireless Sensor Networks are formed by tiny, self-contained, battery-powered computers with radio links that can sense their surroundings for events of interest and store and process the sensed data. Sensor nodes wirelessly communicate with each other to relay information to a central base station. Energy consumption is the most critical parameter in Wireless Sensor Networks (WSNs). Network lifespan is directly influenced by the energy consumption of the sensor nodes. All sensors in the network send and receive data from the base station (BS) using different routing protocols and algorithms. These routing protocols use two main types of clustering: hierarchical clustering and flat clustering. Consequently, effective clustering within Wireless Sensor Network (WSN) protocols is essential for establishing secure connections among nodes, ensuring a stable network lifetime. This paper introduces a novel approach to improve energy efficiency, reduce the length of network connections, and increase network lifetime in heterogeneous Wireless Sensor Networks by employing the K-Nearest Neighbours (KNN) algorithm to optimise node selection and clustering mechanisms for four protocols: Low-Energy Adaptive Clustering Hierarchy (LEACH), Stable Election Protocol (SEP), Threshold-sensitive Energy Efficient sensor Network (TEEN), and Distributed Energy-efficient Clustering (DEC). Simulation results obtained using MATLAB (R2024b) demonstrate the efficacy of the proposed K-Nearest Neighbours algorithm, revealing that the modified protocols achieve shorter distances between cluster heads and nodes, reduced energy consumption, and improved network lifetime compared to the original protocols. The proposed KNN-based approach enhances the network’s operational efficiency and security, offering a robust solution for energy management in WSNs. Full article

Wireless Sensor Networks (WSNs) connected to the Internet of Things (IoT) are increasingly employed in commercial and industrial applications to accomplish various tasks at a low cost. WSNs are essential for gathering diverse types of data within physical environments. A key design objective for WSNs is to balance energy consumption and increase the network’s operating lifetime. Recent studies have shown that mobile base stations (BSs) can significantly extend the lifetime of such networks, especially when their location is optimized using specific criteria. In this study, we propose an algorithm for selecting the optimal BS location in a large network. The algorithm computes a distance metric between sensor nodes (SNs) and potential BS locations on a virtual grid within the WSN. The selection process is repeated periodically to account for dead SNs, allowing the BS to relocate to a new optimal position based on the remaining active nodes after each iteration. Additionally, the inclusion of a relay node (RN) in large networks is explored to improve scalability. The impact of path loss within WSNs is also discussed. The proposed algorithms are applied to the well-known Stable Election Protocol (SEP). Simulation results demonstrate that, compared to other algorithms in the literature, the proposed approaches significantly enhance the lifetime of WSNs. Full article

Unmanned aerial vehicle (UAV)-based wireless sensor networks (WSNs) hold great promise for supporting ground-based sensors due to the mobility of UAVs and the ease of establishing line-of-sight links. UAV-based WSNs equipped with mobile edge computing (MEC) servers effectively mitigate challenges associated with long-distance transmission and the limited coverage of edge base stations (BSs), emerging as a powerful paradigm for both communication and computing services. Furthermore, incorporating simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) as passive relays significantly enhances the propagation environment and service quality of UAV-based WSNs. However, most existing studies place STAR-RISs in fixed positions, ignoring the flexibility of STAR-RISs. Some other studies equip UAVs with STAR-RISs, and UAVs act as flight carriers, ignoring the computing and caching capabilities of UAVs. To address these limitations, we propose an energy-efficient aerial STAR-RIS-aided computing offloading and content caching framework, where we formulate an energy consumption minimization problem to jointly optimize content caching decisions, computing offloading decisions, UAV hovering positions, and STAR-RIS passive beamforming. Given the non-convex nature of this problem, we decompose it into a content caching decision subproblem, a computing offloading decision subproblem, a hovering position subproblem, and a STAR-RIS resource allocation subproblem. We propose a deep reinforcement learning (DRL)–successive convex approximation (SCA) combined algorithm to iteratively achieve near-optimal solutions with low complexity. The numerical results demonstrate that the proposed framework effectively utilizes resources in UAV-based WSNs and significantly reduces overall system energy consumption. Full article

Magnetic induction (MI)-operated wireless sensor networks (WSNs), due to their similar performance in air, underwater, and underground mediums, are rapidly emerging networks that offer a wide range of applications, including mine prevention, power grid maintenance, underground pipeline monitoring, and upstream oil monitoring. MI-based wireless underground sensor networks (WUSNs), utilizing small antenna coils, offer a viable solution by providing consistent channel conditions. The cross-layer protocols address the specific challenges of WUSNs, leading to improved network performance and enhanced operational capabilities in real-world applications. This work proposes a distributed cross-layer solution, leveraging the hybrid marine predator naked mole rat algorithm (MPNMRA) for MI-operated WUSNs. The solution, called DECMN (distributed energy-throughput efficient cross-layer network using MPNMRA), is designed to optimize the MI communication channels, MI relay coils (MI waveguide), and MI waveguide with 3D coils to fulfill quality of service (QoS) parameters, while achieving energy savings and throughput gains. DECMN utilizes the interactions between various layers to develop cross-layer protocols based on MPNMRA. Simulation results demonstrate the effectiveness of DECMN, offering energy savings, increased throughput, and reliable transmissions within the performance limits. Full article

As the Internet of Things (IoT) expands globally, the challenge of signal transmission in remote regions without traditional communication infrastructure becomes prominent. An effective solution involves integrating aerial, terrestrial, and space components to form a Space–Air–Ground Integrated Network (SAGIN). This paper discusses an uplink signal scenario in which various types of data collection sensors as IoT devices use Unmanned Aerial Vehicles (UAVs) as relays to forward signals to low-Earth-orbit satellites. Considering the fairness of resource allocation among IoT devices of the same category, our goal is to maximize the minimum uplink channel capacity for each category of IoT devices, which is a multi-objective optimization problem. Specifically, the variables include the deployment locations of UAVs, bandwidth allocation ratios, and the association between UAVs and IoT devices. To address this problem, we propose a multi-objective evolutionary algorithm that ensures fair resource distribution among multiple parties. The algorithm is validated in eight different scenario settings and compared with various traditional multi-objective optimization algorithms. The experimental results demonstrate that the proposed algorithm can achieve higher-quality Pareto fronts (PFs) and better convergence, indicating more equitable resource allocation and improved algorithmic effectiveness in addressing this issue. Moreover, these pre-prepared, high-quality solutions from PFs provide adaptability to varying requirements in signal collection scenarios. Full article